Multi-modal radiomics model to predict treatment response to neoadjuvant chemotherapy for locally advanced rectal cancer

Table 2 Performance of optimal radiomic signatures

Characteristic	AUC	95%CI	Cut-off	ACC	Specificity	Sensitivity
EMVI
Training	0.73	0.619-0.842	0.331	0.714	0.688	0.773
Validation	0.578	0.426-0.731		0.583	0.594	0.562
CT
Training	0.809	0.745-0.872	0.5	0.818	0.875	0.742
Validation	0.766	0.632-0.899		0.792	0.844	0.688
DCE-T1
Training	0.848	0.79-0.907	0.5	0.818	0.875	0.742
Validation	0.812	0.688-0.937		0.854	0.938	0.688
HR-T2WI
Training	0.845	0.786-0.903	0.5	0.857	0.932	0.758
Validation	0.859	0.746-0.973		0.896	0.969	0.75
ADC
Training	0.847	0.789-0.904	0.5	0.844	0.83	0.864
Validation	0.828	0.71-0.946		0.833	0.844	0.812
CRM
Training	0.921	0.842-1	0.318	0.886	0.854	0.955
Validation	0.908	0.823-0.994		0.812	0.812	0.812
MRM
Training	0.925	0.845-1	0.447	0.886	0.896	0.864
Validation	0.93	0.86-1		0.875	0.875	0.875

EMVI: Extramural venous invasion; CT: Computed tomography; DCE-T1: Dynamic contrast enhanced T1 images; HR-T2WI: High resolution T2-weighted imaging; ADC: Apparent diffusion coefficient; CRM: Combined radiomic model; MRM: Multi-modal radiomics model; AUC: Area under the curve; CI: Confidence interval; ACC: Accuracy.